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19 May 1999 Nonlinear neurons and higher-order statistics: new approaches to human vision and digital image processing
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Abstract
The classical approach in vision research - the derivation of basically linear filter models form experiments with simple artificial test stimuli - is currently undergoing a major revision. Instead of trying to keep the dirty environment out of our clean labs we put it now right into the focus of scientific exploration. The new approach has a close relation to basic engineering strategies for electronic image processing since its major concept is the exploration of the statistical redundancies of the environment by appropriate neural transformations. The standard engineering methods are not sufficient, however. Even a basic biological feature like orientation selectivity requires the consideration of higher-order statistics, like cumulants or polyspectra. Furthermore, there exists an abundance of nonlinear phenomena in biological vision, for example the phase-invariance of complex cells, cortical gain control, or end-stopping, which make it necessary to consider unconventional modeling approaches like differential geometry or Volterra-Wiener system. By use of such methods we cannot only gain a deeper understanding of the adaption of the visual system to the complex natural environment, but we can also make the biological system an inspiring source for the design of novel strategies in electronic image processing.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christoph Zetzsche and Gerhard Krieger "Nonlinear neurons and higher-order statistics: new approaches to human vision and digital image processing", Proc. SPIE 3644, Human Vision and Electronic Imaging IV, (19 May 1999); https://doi.org/10.1117/12.348430
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